ArticleCancers2022
Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.NPJ precision oncology · 2026Article
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.European journal of medical research · 2025Review
- Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.International journal of clinical pharmacy · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Adverse event profile of five anti head and neck squamous cell carcinoma drugs: a descriptive analysis from WHO-VigiAccess.Frontiers in pharmacology · 2025Article
- Review
- Predictive Modeling of Drug-Related Adverse Events with Real-World Data: A Case Study of Linezolid Hematologic Outcomes.Clinical pharmacology and therapeutics · 2024Article
- Article
- Circulomic variables predict pathologic staging preoperatively in treatment-naïve non-small cell lung cancer.Journal of thoracic disease · 2023Article
- The artificial intelligence and machine learning in lung cancer immunotherapy.Journal of hematology & oncology · 2023Review
- Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer.Frontiers in oncology · 2023Review
- Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced-stage EGFR-mutated non-small-cell lung cancer.Thoracic cancer · 2022Article
- Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review.Biomedicines · 2022Review
- Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.Frontiers in oncology · 2022Article
- Machine learning based survival prediction in Glioma using large-scale registry data.Health informatics journalArticle
Corrections and comments
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.
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